# Rmarkdown to docx instructions
# https://rmarkdown.rstudio.com/articles_docx.html
# https://bookdown.org/yihui/rmarkdown/word-document.html
# R-Markdown: The Definitive Guide
# https://bookdown.org/yihui/rmarkdown/
9-15-21 Data collection in field Setup at organic farm at 11:54am. Site chosen near apple trees.
Station 2, .1L / 30m. 12:05pm, 12:35pm, 1:05pm applications Station 3, .5L / 30m. 12:00pm, 12:30pm, 1:00pm applications
df_names <- c("TIMESTAMP", "RECORD", "BattV_Avg", "PTemp_C_Avg", "VWC_Avg", "EC_Avg", "T_Avg", "P_Avg", "PA_Avg", "VR_Avg", "station", "water_L")
# Station 2, .1L / 30m. 12:05pm, 12:35pm, 1:05pm applications
# was cleared before collection
station2_df <- read_csv("Lab2_data/CR300Series_2_Table1 - Copy.dat", skip=6, col_names=FALSE) %>%
mutate(station = 2, water_L = .1)
## Rows: 19 Columns: 10
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## dbl (9): X2, X3, X4, X5, X6, X7, X8, X9, X10
## dttm (1): X1
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
# Station 3, .5L / 30m. 12:00pm, 12:30pm, 1:00pm applications
# station 3 had data from previous runs on it, needs to be filtered out
station3_df <-read_csv("Lab2_data/CR300Series_3_Table1 - Copy.dat", skip=610, col_names=FALSE) %>%
mutate(station = 3, water_L = .5)
## Rows: 21 Columns: 10
## -- Column specification --------------------------------------------------------
## Delimiter: ","
## dbl (9): X2, X3, X4, X5, X6, X7, X8, X9, X10
## dttm (1): X1
##
## i Use `spec()` to retrieve the full column specification for this data.
## i Specify the column types or set `show_col_types = FALSE` to quiet this message.
# names of columns
names(station2_df) <- df_names
names(station3_df) <- df_names
# combine them
my_df <- bind_rows(station2_df, station3_df)
Topic: Integration of CR310 Data Logger with soil sensors with data logger for experimentation and wireless data acquisition
Field experiment set-up: * configured
# Flow Chart in R using DiagrammeR package, docs: https://rich-iannone.github.io/DiagrammeR/docs.html
# create a flow chart
grViz(diagram = "digraph dot {
graph [layout = dot]
# define node aesthetics
node [fontname = arial,
shape = oval,
color = gray,
style = filled,
fontcolor = White,
fontsize = 11]
tab1 [label = '@@1']
tab2 [label = '@@2']
tab3 [label = '@@3']
tab4 [label = '@@4']
tab5 [label = '@@5']
tab6 [label = '@@6']
# set up node layout
tab1 -> tab2
tab2 -> tab3
tab2 -> tab6
tab6 -> tab2
tab6 -> tab4
tab4 -> tab5
tab5 -> tab1
tab3 -> tab1
}
# define tab labels
[1]: 'Learning Data Science to better fight a walrus'
[2]: 'fighting a walrus'
[3]: 'lose the fight'
[4]: 'laser swords run out of batteries'
[5]: 'go to store and get batteries'
[6]: 'win a laser sword as loot'
")
# DiagrammeR mermaid graph
mermaid("
graph LR
A(Learning Data Science to better fight a walrus)-->B
A-->C[lose the fight]
C-->A
C-->E(taco break)
B[fighting a walrus]-->D{laser swords run out of batteries}
C-->D(go to store and get batteries)
D-->F
E-->F{win a laser sword as loot}
")
mermaid("graph.mmd")
# Sequence Diagrams, as seen in "How to Draw Sequence Diagrams" report by Poranen, Makinen, and Nummenmaa
# offers a good introduction to sequence diagrams.
#Let's replicate the ticket-buying example from Figure 1 of this report and add in some conditionals.
mermaid("
sequenceDiagram
customer->>ticket seller: ask ticket
ticket seller->>database: seats
alt tickets available
database->>ticket seller: ok
ticket seller->>customer: confirm
customer->>ticket seller: ok
ticket seller->>database: book a seat
ticket seller->>printer: print ticket
else sold out
database->>ticket seller: none left
ticket seller->>customer: sorry
end
")
mermaid("graph.mmd")